Skip to main content

Advertisement

Log in

A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients

  • Pharmacokinetics and Disposition
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route.

Methods

We used 10-years pediatric HSCT patients’ dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two.

Results

The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively.

Conclusion

The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Bleyzac N (2008) The use of pharmacokinetic models in paediatric onco-haematology: effects on clinical outcome through the examples of busulfan and cyclosporine. Fundam Clin Pharmacol 22(6):605–608. https://doi.org/10.1111/j.1472-8206.2008.00652.x

    Article  CAS  PubMed  Google Scholar 

  2. Welling PG, Tse FL (1984) Factors contributing to variability in drug pharmacokinetics. I. Absorption. J Clin Hosp Pharm 9(3):163–179

    CAS  PubMed  Google Scholar 

  3. Jacobson PA, Ng J, Green KGE, Rogosheske J, Brundage R (2003) Posttransplant day significantly influences pharmacokinetics of cyclosporine after hematopoietic stem cell transplantation. Biol Blood Marrow Transplant 9(5):304–311

    Article  CAS  PubMed  Google Scholar 

  4. Ruutu T, Gratwohl A, de Witte T, Afanasyev B, Apperley J, Bacigalupo A, Dazzi F, Dreger P, Duarte R, Finke J, Garderet L, Greinix H, Holler E, Kroger N, Lawitschka A, Mohty M, Nagler A, Passweg J, Ringden O, Socie G, Sierra J, Sureda A, Wiktor-Jedrzejczak W, Madrigal A, Niederwieser D (2014) Prophylaxis and treatment of GVHD: EBMT-ELN working group recommendations for a standardized practice. Bone Marrow Transplant 49(2):168–173. https://doi.org/10.1038/bmt.2013.107

    Article  CAS  PubMed  Google Scholar 

  5. Kahan BD (2004) Therapeutic drug monitoring of cyclosporine: 20 years of progress. Transplant Proc 36(2 Suppl):378S–391S. https://doi.org/10.1016/j.transproceed.2004.01.091

    Article  CAS  PubMed  Google Scholar 

  6. Martin P, Bleyzac N, Souillet G, Galambrun C, Bertrand Y, Maire PH, Jelliffe RW, Aulagner G (2003) Relationship between CsA trough blood concentration and severity of acute graft-versus-host disease after paediatric stem cell transplantation from matched-sibling or unrelated donors. Bone Marrow Transplant 32(8):777–784. https://doi.org/10.1038/sj.bmt.1704213

    Article  CAS  PubMed  Google Scholar 

  7. Schechter T, Lewis VA, Schultz KR, Mitchell D, Chen S, Seto W, Teuffel O, Gibson P, Doyle JJ, Gassas A, Sung L, Lee Dupuis L (2018) Relationship between cyclosporine area-under-the curve and acute graft versus host disease in pediatric patients undergoing hematopoietic stem cell transplant: a prospective, multicenter study. Pediatr Hematol Oncol 35(4):288–296. https://doi.org/10.1080/08880018.2018.1520948

    Article  CAS  PubMed  Google Scholar 

  8. Willemze AJ, Press RR, Lankester AC, Egeler RM, den Hartigh J, Vossen JM (2010) CsA exposure is associated with acute GVHD and relapse in children after SCT. Bone Marrow Transplant 45(6):1056–1061. https://doi.org/10.1038/bmt.2009.299

    Article  CAS  PubMed  Google Scholar 

  9. Bleyzac N, Cuzzubbo D, Renard C, Garnier N, Dubois V, Domenech C, Goutagny MP, Plesa A, Grardel N, Goutelle S, Janoly-Dumenil A, Bertrand Y (2016) Improved outcome of children transplanted for high-risk leukemia by using a new strategy of cyclosporine-based GVHD prophylaxis. Bone Marrow Transplant 51(5):698–704. https://doi.org/10.1038/bmt.2015.350

    Article  CAS  PubMed  Google Scholar 

  10. Inoue Y, Saito T, Ogawa K, Nishio Y, Kosugi S, Suzuki Y, Kato M, Sakai H, Takahashi M, Miura I (2012) Pharmacokinetics of cyclosporine a conversion from twice-daily infusion to oral administration in allogeneic hematopoietic stem cell transplantation.

  11. Ku YM, Min DI, Flanigan M (1998) Effect of grapefruit juice on the pharmacokinetics of microemulsion cyclosporine and its metabolite in healthy volunteers: does the formulation difference matter? J Clin Pharmacol 38(10):959–965

    Article  CAS  PubMed  Google Scholar 

  12. Kimura S, Oshima K, Okuda S, Sato K, Sato M, Terasako K, Nakasone H, Kako S, Yamazaki R, Tanaka Y, Tanihara A, Higuchi T, Nishida J, Kanda Y (2010) Pharmacokinetics of CsA during the switch from continuous intravenous infusion to oral administration after allogeneic hematopoietic stem cell transplantation. Bone Marrow Transplant 45(6):1088–1094. https://doi.org/10.1038/bmt.2009.316

    Article  CAS  PubMed  Google Scholar 

  13. Choi JS, Lee SH, Chung SJ, Yoo KH, Sung KW, Koo HH (2006) Assessment of converting from intravenous to oral administration of cyclosporin A in pediatric allogeneic hematopoietic stem cell transplant recipients. Bone Marrow Transplant 38(1):29–35. https://doi.org/10.1038/sj.bmt.1705402

    Article  CAS  PubMed  Google Scholar 

  14. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4

    Article  PubMed  Google Scholar 

  15. Khayi F, Lafarge L, Terret C, Albrand G, Falquet B, Culine S, Gourgou S, Ducher M, Bourguignon L (2019) Prediction of docetaxel toxicity in older cancer patients: a Bayesian network approach. Fundam Clin Pharmacol DOI 33:679–686. https://doi.org/10.1111/fcp.12476

    Article  CAS  Google Scholar 

  16. Leclerc V, Ducher M, Bleyzac N (2018) Bayesian networks: a new approach to predict therapeutic range achievement of initial cyclosporine blood concentration after pediatric hematopoietic stem cell transplantation. Drugs R D 18(1):67–75. https://doi.org/10.1007/s40268-017-0223-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bernard E, Goutelle S, Bertrand Y, Bleyzac N (2014) Pharmacokinetic drug-drug interaction of calcium channel blockers with cyclosporine in hematopoietic stem cell transplant children. Ann Pharmacother 48(12):1580–1584. https://doi.org/10.1177/1060028014550644

    Article  CAS  PubMed  Google Scholar 

  18. Bernard E, Mialou V, Dony A, Garnier N, Renard C, Bleyzac N (2014) Lacidipine efficacy and safety for high blood pressure treatment in pediatric oncohematology. Arch Pediatr 21(10):1101–1105. https://doi.org/10.1016/j.arcped.2014.06.028

    Article  CAS  PubMed  Google Scholar 

  19. Dessars B, Cotton F, Thiry P, Gulbis B (2003) Comparison of automated ACMIA and EMIT immunoassays for whole blood cyclosporin monitoring. Clin Lab 49(3-4):135–140

    CAS  PubMed  Google Scholar 

  20. Kurgan LA, Cios KJ (2004) CAIM discretization algorithm. IEEE Trans Knowl Data Eng 16(2):145–153. https://doi.org/10.1109/TKDE.2004.1269594

    Article  Google Scholar 

  21. Hesselink DA, van Schaik RHN, Nauta J, van Gelder T (2008) A drug transporter for all ages? ABCB1 and the developmental pharmacogenetics of cyclosporine. Pharmacogenomics 9(6):783–789. https://doi.org/10.2217/14622416.9.6.783

    Article  CAS  PubMed  Google Scholar 

  22. de Wildt SN, Kearns GL, Leeder JS, van den Anker JN (1999) Cytochrome P450 3A: ontogeny and drug disposition. Clin Pharmacokinet 37(6):485–505. https://doi.org/10.2165/00003088-199937060-00004

    Article  PubMed  Google Scholar 

  23. Kanamori M, Takahashi H, Echizen H (2002) Developmental changes in the liver weight- and body weight-normalized clearance of theophylline, phenytoin and cyclosporine in children. Int J Clin Pharmacol Ther 40(11):485–492

    Article  CAS  PubMed  Google Scholar 

  24. Hakkola J, Tanaka E, Pelkonen O (1998) Developmental expression of cytochrome P450 enzymes in human liver. Pharmacol Toxicol 82(5):209–217

    Article  CAS  PubMed  Google Scholar 

  25. Fanta S, Jonsson S, Backman JT, Karlsson MO, Hoppu K (2007) Developmental pharmacokinetics of ciclosporin--a population pharmacokinetic study in paediatric renal transplant candidates. Br J Clin Pharmacol 64(6):772–784. https://doi.org/10.1111/j.1365-2125.2007.03003.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fakhoury M, Litalien C, Medard Y, Cavé H, Ezzahir N, Peuchmaur M, Jacqz-Aigrain E (2005) Localization and mRNA expression of CYP3A and P-glycoprotein in human duodenum as a function of age. Drug Metab Dispos 33(11):1603–1607. https://doi.org/10.1124/dmd.105.005611

    Article  CAS  PubMed  Google Scholar 

  27. Bouillon-Pichault M, Jullien V, Bazzoli C, Pons G, Tod M (2011) Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4. J Pharmacokinet Pharmacodyn 38(1):25–40. https://doi.org/10.1007/s10928-010-9173-1

    Article  CAS  PubMed  Google Scholar 

  28. Sullivan KM, Mei Z, Grummer-Strawn L, Parvanta I (2008) Haemoglobin adjustments to define anaemia. Tropical Med Int Health 13(10):1267–1271. https://doi.org/10.1111/j.1365-3156.2008.02143.x

    Article  CAS  Google Scholar 

  29. Bercovitz RS, Quinones RR (2013) A survey of transfusion practices in pediatric hematopoietic stem cell transplant patients. J Pediatr Hematol Oncol 35(2):e60–e63. https://doi.org/10.1097/MPH.0b013e3182707ae5

    Article  PubMed  Google Scholar 

  30. Atiyeh BA, Dabbagh SS, Gruskin AB (1996) Evaluation of renal function during childhood. Pediatr Rev 17(5):175–180

    Article  CAS  PubMed  Google Scholar 

  31. McNeer JL, Kletzel M, Rademaker A, Alford K, O'Day K, Schaefer C, Duerst R, Jacobsohn DA (2010) Early elevation of C-reactive protein correlates with severe infection and nonrelapse mortality in children undergoing allogeneic stem cell transplantation. Biol Blood Marrow Transplant 16(3):350–357. https://doi.org/10.1016/j.bbmt.2009.10.036

    Article  CAS  PubMed  Google Scholar 

  32. Fuji S, Kim S-W, Fukuda T, S-i M, Yamasaki S, Morita-Hoshi Y, Ohara-Waki F, Heike Y, Tobinai K, Tanosaki R, Takaue Y (2008) Preengraftment serum C-reactive protein (CRP) value may predict acute graft-versus-host disease and nonrelapse mortality after allogeneic hematopoietic stem cell transplantation. Biol Blood Marrow Transplant 14(5):510–517. https://doi.org/10.1016/j.bbmt.2008.02.008

    Article  CAS  PubMed  Google Scholar 

  33. Wilhelm AJ, de Graaf P, Veldkamp AI, Janssen JJWM, Huijgens PC, Swart EL (2012) Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy. Br J Clin Pharmacol 73(4):553–563. https://doi.org/10.1111/j.1365-2125.2011.04116.x

    Article  CAS  PubMed  Google Scholar 

  34. Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A (2013) Orange: data mining toolbox in Python. J Mach Learn Res 14(1):2349–2353

    Google Scholar 

  35. Bouckaert RR (2004) Bayesian network classifiers in Weka. In: ed. Department of Computer Science.

  36. Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347. https://doi.org/10.1007/BF00994110

    Article  Google Scholar 

  37. Chow C, Liu C (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans Inf Theory 14(3):462–467. https://doi.org/10.1109/TIT.1968.1054142

    Article  Google Scholar 

  38. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann

  39. Lindholm A (1991) Factors influencing the pharmacokinetics of cyclosporine in man. Ther Drug Monit 13(6):465–477

    Article  CAS  PubMed  Google Scholar 

  40. Ptachcinski RJ, Venkataramanan R, Burckart GJ (1986) Clinical pharmacokinetics of cyclosporin. Clin Pharmacokinet 11(2):107–132. https://doi.org/10.2165/00003088-198611020-00002

    Article  CAS  PubMed  Google Scholar 

  41. Lindholm A, Henricsson S, Lind M, Dahlqvist R (1988) Intraindividual variability in the relative systemic availability of cyclosporin after oral dosing. Eur J Clin Pharmacol 34(5):461–464

    Article  CAS  PubMed  Google Scholar 

  42. Shaw LM (1989) Advances in cyclosporine pharmacology, measurement, and therapeutic monitoring. Clin Chem 35(7):1299–1308

    Article  CAS  PubMed  Google Scholar 

  43. Schultz KR, Nevill TJ, Toze CL, Corr T, Currie C, Strong DK, Keown PA (1998) The pharmacokinetics of oral cyclosporin A (Neoral) during the first month after bone marrow transplantation. Transplant Proc 30(5):1668–1670

    Article  CAS  PubMed  Google Scholar 

  44. Schultz KR, Nevill TJ, Balshaw RF, Toze CL, Corr T, Currie CJ, Strong DK, Keown PA (2000) Effect of gastrointestinal inflammation and age on the pharmacokinetics of oral microemulsion cyclosporin A in the first month after bone marrow transplantation. Bone Marrow Transplant 26(5):545–551. https://doi.org/10.1038/sj.bmt.1702545

    Article  CAS  PubMed  Google Scholar 

  45. Inoue Y, Saito T, Ogawa K, Nishio Y, Kosugi S, Suzuki Y, Shibuya Y, Kato M, Takahashi M, Miura I (2011) Pharmacokinetics of cyclosporine A at a high-peak concentration of twice-daily infusion and oral administration in allogeneic haematopoietic stem cell transplantation. J Clin Pharm Ther 36(4):518–524. https://doi.org/10.1111/j.1365-2710.2010.01199.x

    Article  CAS  PubMed  Google Scholar 

  46. Hogan WJ, Storb R (2004) Use of cyclosporine in hematopoietic cell transplantation. Transplant Proc 36(2 Suppl):367S–371S. https://doi.org/10.1016/j.transproceed.2004.01.043

    Article  CAS  PubMed  Google Scholar 

  47. Colombo D, Ammirati E (2011) Cyclosporine in transplantation - a history of converging timelines. J Biol Regul Homeost Agents 25(4):493–504

    CAS  PubMed  Google Scholar 

  48. Cooney GF, Habucky K, Hoppu K (1997) Cyclosporin pharmacokinetics in paediatric transplant recipients. Clin Pharmacokinet 32(6):481–495. https://doi.org/10.2165/00003088-199732060-00004

    Article  CAS  PubMed  Google Scholar 

  49. Parquet N, Reigneau O, Humbert H, Guignard M, Ribaud P, Socié G, Devergie A, Espérou H, Gluckman E (2000) New oral formulation of cyclosporin A (Neoral) pharmacokinetics in allogeneic bone marrow transplant recipients. Bone Marrow Transplant 25(9):965–968. https://doi.org/10.1038/sj.bmt.1702375

    Article  CAS  PubMed  Google Scholar 

  50. Wallemacq PE, Reding R, Sokal EM, de Ville de Goyet J, Clement de Clety S, Van Leeuw V, De Backer M, Otte JB (1997) Clinical pharmacokinetics of Neoral in pediatric recipients of primary liver transplants. Transpl Int 10(6):466–470. https://doi.org/10.1007/s001470050088

    Article  CAS  PubMed  Google Scholar 

  51. Dunn S (2000) Neoral use in the pediatric transplant recipient. Transplant Proc 32 (3A Suppl): 20s-26s. https://doi.org/10.1016/s0041-1345(00)00861-7

  52. Aitken C, Mavridis D (2019) Reasoning under uncertainty. Evidence-based mental health 22(1):44–48. https://doi.org/10.1136/ebmental-2018-300074

    Article  PubMed  Google Scholar 

  53. Nistal-Nuno B (2018) Tutorial of the probabilistic methods Bayesian networks and influence diagrams applied to medicine. J Eviden Based Med 11(2):112–124. https://doi.org/10.1111/jebm.12298

    Article  Google Scholar 

  54. Lee KJ, Carlin JB (2012) Recovery of information from multiple imputation: a simulation study. Emerging themes in epidemiology 9(1):3. https://doi.org/10.1186/1742-7622-9-3

    Article  PubMed  PubMed Central  Google Scholar 

  55. Lee KJ, Simpson JA (2014) Introduction to multiple imputation for dealing with missing data. Respirology (Carlton, Vic) 19(2):162–167. https://doi.org/10.1111/resp.12226

    Article  Google Scholar 

  56. Yang Y, Webb GI (2009) Discretization for naive-Bayes learning: managing discretization bias and variance. Mach Learn 74(1):39–74. https://doi.org/10.1007/s10994-008-5083-5

    Article  Google Scholar 

  57. Andreassen S, Jensen FV, Olesen KG (1991) Medical expert systems based on causal probabilistic networks. Int J Biomed Comput 28(1-2):1–30

    Article  CAS  PubMed  Google Scholar 

  58. Onisko A, Druzdzel MJ, Austin RM (2019) Application of Bayesian network modeling to pathology informatics. Diagn Cytopathol 47(1):41–47. https://doi.org/10.1002/dc.23993

    Article  PubMed  Google Scholar 

  59. Flores CD, Fonseca JM, Bez MR, Respicio A, Coelho H (2014) Method for building a medical training simulator with bayesian networks: SimDeCS. Stud Health Technol Inform 207:102–114

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

Doctor Vincent Leclerc participated to the design of the study, the data production, the data analysis, manuscript writing, and manuscript reviewing. Doctor Michel Ducher participated to the design of the study, the data analysis, manuscript writing, and manuscript reviewing. Doctor Antony Ceraulo participated to the design of the study, the data production, and the manuscript reviewing. Professor Yves Bertrand participated to the design of the study, the data production, and the manuscript reviewing. Doctor Nathalie Bleyzac participated to the design of the study, the data production, manuscript writing, and manuscript reviewing.

Corresponding author

Correspondence to Vincent Leclerc.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 16 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leclerc, V., Bleyzac, N., Ceraulo, A. et al. A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients. Eur J Clin Pharmacol 76, 1409–1416 (2020). https://doi.org/10.1007/s00228-020-02918-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00228-020-02918-9

Keywords

Navigation